Bottom Line:
Propagated ROIs were quantitatively compared with expert physician-drawn ROIs on the per-treatment scan using Dice scores and mean slicewise Hausdorff distances, and center of mass distances for GTVs.DIR was successfully used on 22 patients to propagate target and OAR structures for ART with good anatomical agreement for OARs.It is recommended that propagated target structures be thoroughly reviewed by the treating physician.

Background: Adaptive Radiotherapy aims to identify anatomical deviations during a radiotherapy course and modify the treatment plan to maintain treatment objectives. This requires regions of interest (ROIs) to be defined using the most recent imaging data. This study investigates the clinical utility of using deformable image registration (DIR) to automatically propagate ROIs.

Methods: Target (GTV) and organ-at-risk (OAR) ROIs were non-rigidly propagated from a planning CT scan to a per-treatment CT scan for 22 patients. Propagated ROIs were quantitatively compared with expert physician-drawn ROIs on the per-treatment scan using Dice scores and mean slicewise Hausdorff distances, and center of mass distances for GTVs. The propagated ROIs were qualitatively examined by experts and scored based on their clinical utility.

Results: Good agreement between the DIR-propagated ROIs and expert-drawn ROIs was observed based on the metrics used. 94% of all ROIs generated using DIR were scored as being clinically useful, requiring minimal or no edits. However, 27% (12/44) of the GTVs required major edits.

Conclusion: DIR was successfully used on 22 patients to propagate target and OAR structures for ART with good anatomical agreement for OARs. It is recommended that propagated target structures be thoroughly reviewed by the treating physician.

Figure 4: Histograms of (a) & (c) the Dice scores and (b) & (d) MSHDs for all of the OARs and GTVs grouped into expert scoring category. The frequencies are normalized to the total number of OARs and GTVs scored in the study (172 and 44 respectively).

Mentions:
Expert physician scores are shown in Figure 3. Although these scores are subjective in that they are based on the opinions of the expert physician, the authors feel that this ultimately represents the clinical utility of the automatically generated ROIs. The scores show that despite some disagreement between DIR-propagated and expert physician-drawn ROIs on the per-treatment CT scans, the majority (202/216 = 94%) of the DIR-propagated ROIs were considered useful and required no or minor changes. The majority of ROIs scored as being not useful or requiring major edits were GTVs. The relationship between the ROI metric scores and the physician scores was investigated. Figure 4 shows histograms of the OARs metric scores grouped into expert physician score category. When comparing only the groups with scores of 1 or 2 with 3 for the OARs, there is a moderate correlation between both Dice score and MSHD and the expert physician score (point biserial correlation rpb = −0.319, p < 0.0001 & rpb = 0.341, p = 0.0001 for Dice scores and MSHDs respectively). For the GTVs, there was no correlation between the Dice and MSHD and expert physician scores (point biserial correlation rpb = −0.002, p = 0.49 & rpb = 0.185, p = 0.17 for Dice scores and MSHDs respectively), however these values are not statistically significant, most likely due to too few GTV samples. Figure 4 suggests that the metrics used in this study have clinical relevance for OARs, but not necessarily for GTVs. This is most likely due to the subjective definition of GTVs by the physician that is based on clinical knowledge and experience rather than pure image intensity values.

Figure 4: Histograms of (a) & (c) the Dice scores and (b) & (d) MSHDs for all of the OARs and GTVs grouped into expert scoring category. The frequencies are normalized to the total number of OARs and GTVs scored in the study (172 and 44 respectively).

Mentions:
Expert physician scores are shown in Figure 3. Although these scores are subjective in that they are based on the opinions of the expert physician, the authors feel that this ultimately represents the clinical utility of the automatically generated ROIs. The scores show that despite some disagreement between DIR-propagated and expert physician-drawn ROIs on the per-treatment CT scans, the majority (202/216 = 94%) of the DIR-propagated ROIs were considered useful and required no or minor changes. The majority of ROIs scored as being not useful or requiring major edits were GTVs. The relationship between the ROI metric scores and the physician scores was investigated. Figure 4 shows histograms of the OARs metric scores grouped into expert physician score category. When comparing only the groups with scores of 1 or 2 with 3 for the OARs, there is a moderate correlation between both Dice score and MSHD and the expert physician score (point biserial correlation rpb = −0.319, p < 0.0001 & rpb = 0.341, p = 0.0001 for Dice scores and MSHDs respectively). For the GTVs, there was no correlation between the Dice and MSHD and expert physician scores (point biserial correlation rpb = −0.002, p = 0.49 & rpb = 0.185, p = 0.17 for Dice scores and MSHDs respectively), however these values are not statistically significant, most likely due to too few GTV samples. Figure 4 suggests that the metrics used in this study have clinical relevance for OARs, but not necessarily for GTVs. This is most likely due to the subjective definition of GTVs by the physician that is based on clinical knowledge and experience rather than pure image intensity values.

Bottom Line:
Propagated ROIs were quantitatively compared with expert physician-drawn ROIs on the per-treatment scan using Dice scores and mean slicewise Hausdorff distances, and center of mass distances for GTVs.DIR was successfully used on 22 patients to propagate target and OAR structures for ART with good anatomical agreement for OARs.It is recommended that propagated target structures be thoroughly reviewed by the treating physician.

Background: Adaptive Radiotherapy aims to identify anatomical deviations during a radiotherapy course and modify the treatment plan to maintain treatment objectives. This requires regions of interest (ROIs) to be defined using the most recent imaging data. This study investigates the clinical utility of using deformable image registration (DIR) to automatically propagate ROIs.

Methods: Target (GTV) and organ-at-risk (OAR) ROIs were non-rigidly propagated from a planning CT scan to a per-treatment CT scan for 22 patients. Propagated ROIs were quantitatively compared with expert physician-drawn ROIs on the per-treatment scan using Dice scores and mean slicewise Hausdorff distances, and center of mass distances for GTVs. The propagated ROIs were qualitatively examined by experts and scored based on their clinical utility.

Results: Good agreement between the DIR-propagated ROIs and expert-drawn ROIs was observed based on the metrics used. 94% of all ROIs generated using DIR were scored as being clinically useful, requiring minimal or no edits. However, 27% (12/44) of the GTVs required major edits.

Conclusion: DIR was successfully used on 22 patients to propagate target and OAR structures for ART with good anatomical agreement for OARs. It is recommended that propagated target structures be thoroughly reviewed by the treating physician.